Omniture’s Bill Mungovan Interviewed by Eric Enge

Bill Mungovan is currently the director of product marketing at Omniture responsible for their SearchCenter product. Previously, he helped build the search engine marketing practice at Carat, working directly with agency clients in exceeding their ROI and branding goals through search.

Mungovan brings a broad range of skills in search advertising optimization, account management, search directory development and search content production to his role. He previously served as Director, Client Relations at LookSmart where he was overseeing the day-to-day operations of the Account Management and Customer Service teams.

Prior to joining LookSmart, Mungovan worked for Snap/NBCi in the Search and Directory space, and also worked at CNET in San Francisco.

Mungovan has shared his search expertise as an invited panelist at several industry events, including Search Engine Strategies in New York, San Jose, Chicago and Dallas, PubCon and OMMA West. He holds an MBA from the University of San Francisco and a B.A. from the University of New Hampshire.

Interview Transcript

Eric Enge: Let’s start by talking about a basic overview of Omniture, and then move into an overview of Omniture SearchCenter.

Bill Mungovan: Omniture is a general value proposition to the market. Our Online Marketing Suite includes our web analytics tool, SiteCatalyst. It also includes 9 other products, such as Genesis, an integration tool that pulls in data from other sources and Test&Target, a landing page optimization and a multivariate testing tool, and of course SearchCenter.

Eric Enge: Is Test&Target based off the acquisition of Offermatica?

Bill Mungovan: Yes, Test&Target is basically based off of the Offermatica technology. It is a dynamic Landing Page Optimization with Multivariate Testing on its landing pages.

SearchCenter was basically built in the context of that marketing suite. SearchCenter is what we call a search management tool, in that it accesses each of the major search engines from a single location and provides automated bid management and portfolio optimization. You can access all sorts of different reporting functionalities through the SiteCatalyst integration.

We think about SearchCenter as a tool for search marketers, but given the fact that we have Genesis, we can pull data in from other sources, like an email provider, an ad-server, a CRM system like SalesForce.com or a client’s custom, internal database.

Eric Enge: Right. Pulling in data from other sources is one of the big challenges with bid or campaign management. People are so used to treating everything like they are direct response marketers. But a company that has physical locations, and a web site, is likely going to have interactions with people going to the web site and buying offline, and vice versa. So, being able to pull in data from other sources allows you to credit those campaigns in a meaningful way so that you can more effectively manage your bidding strategy.

Bill Mungovan: Yes. That’s the heart and soul of the way we think about search, which is obviously the hot topic. We use SiteCatalyst to collect all of that data. So, in your example, you could pull in point-of-sale data or data from a call center. There is really no shortage of examples there. Then we can generate bid rules and bid strategies based on that data.

That’s how Omniture thinks about the world, given the fact that we have SiteCatalyst as an underlying platform. We can pull data in from all these different sources, and then use that data not just for attribution, but also to improve bid strategies.

We have clients whose web sites generate more sales over the phone than they do on the actual site itself. Say they sell complicated items that people want to talk through over the phone. We need to be able to tie back exactly which keywords led to sales over the phone, and how much those sales were worth. So, it’s not just attributing a sale to the correct channel, it’s actually determining bidding based on that data.

Eric Enge: You made reference to portfolio management, and there is an aspect of that that I’d like to dig into a little bit, which is the notion that if you are bidding on a very high volume keyword it’s really easy to get enough data to make decisions about whether that keyword is profitable or not.

But, we have the long tail, where the data is scarcer. It’s maybe only a few clicks a day, or maybe it’s a large pay-per-click account that has hundreds of thousands of keywords that get a few clicks every a week. So, by portfolio management, do you mean a strategy for looking at those keywords in a more holistic group fashion?

Bill Mungovan: Yes, that’s exactly what it is. It’s just an option for us to have two types of bid management in the system. One is the bid rules, which are basically just if-then statements. So, if you are getting this much revenue from a keyword, then you should increase the CPC by a little bit. But we also have portfolio optimization on the other side, which is just another option for marketers.

We found that having both presents more options to our advertisers. Now, with respect to the question of not having enough data to actually understand what’s happening on a keyword by keyword basis for long tail keywords can happen. That’s the biggest fundamental problem with portfolios of keywords.

I think the portfolio optimization approach does not have enough data, and our tool projects it out based on what limited data we have and what we think may happen in the future. If there is no data, there is only so much we can go on. After a certain point, we just assume that that keyword is just not going to generate any clicks. But, that’s one of the problems that we see. We do mathematical projections for the future based on the limited data that we have.

What I am getting at is that our approach to search is the opposite of complicated mathematical Black Box formulas. We also have that built into the tool, we just don’t believe fundamentally within Omniture that you can click a button and your entire search marketing program will be quickly taken care of. That’s a Black Box approach that we feel has run its course in the market.

We just don’t believe that there is any single approach to bid management or search marketing that’s going to work for many different clients. It speaks to the broader vision in which we view search, which is that we are not an agency, but we have agency services within Omniture.

Our goal is really to be as transparent as possible to our customers. Transparency is a key issue for us, as we have many clients who have us manage their search program for a very short period of time, about three to six months. Then we coach them along the way on how and what we are doing.

We get them up to a certain level of performance, and then give that over to them in-house. So, not relying on service revenue the way an agency might works to our advantage, because we can give full transparency to our clients. And that model has been working pretty well for us.

So, to get back to the portfolio question, the idea of us being able to take care of the whole thing for you is just impossible in our mind.

Eric Enge: Right. Can you give me a set of things that you are managing? There may be ten keywords that are producing great volume, another fifty that are producing marginal volume and then some that produce less than 10% of the volume of the high-volume keywords. You still want to be able to manage those less than 10% keywords at some level, correct?

Bill Mungovan: Yes, and that’s an area where you would apply different rules to the different types of keywords. One thing we tell clients a lot is there is no faster way to lose money in search marketing than to set up the wrong portfolio or to really have poor performing keywords dragging down the average of some of your best performing keywords.

Similarly, you wouldn’t necessarily want your highest volume keywords in the same portfolio as your lowest volume keywords. You may, depending on what the keywords actually are. But you may not, and we want our clients to be fairly careful about how they set up the rules in their portfolios if they are, in fact, using that particular feature. You may actually give more credit to certain keywords because those at higher volumes are doing all the heavy lifting.

Eric Enge: Let’s dig a little bit into the announcement you had recently with Scotts MiracleGro.

Bill Mungovan: In general, we are doing more and more deals within Omniture, both in the SearchCenter business and in other pieces of our business that involve multiple products. And Scotts is a good example of that, because essentially there is only so much you can do if you just think about search engine marketing as a silo. So, by bringing in data from other sources and using it effectively, we opened up a lot of different options for search campaigns. That’s what Scotts is trying to do.

We don’t have results for this particular example, just because it’s a new announcement for us. But, in general they were having a hard time understanding exactly how email marketing campaigns could be used to remarket. And they also want to know how email marketing may have impacted or not impacted what happened on their site and what happened in their search marketing program.

Scotts was trying to take a more holistic view of their online business optimization efforts. They made the choice to stop thinking about email as one silo and search as a separate silo. And so, by using SiteCatalyst as their platform, they used Omniture Genesis to pull in the ExactTarget data, and SearchCenter for their search data, and measure it all in one place.

Eric Enge: Right. So interactions can be more easily understood.

Bill Mungovan: Correct. And, on a related note, they had problems with what they called Post-Click Behavior, which is basically visitor engagement and what happens on their site once they attract a customer. They’ve stopped thinking about email and search as just visitor acquisition tools, and started to think about the whole thing holistically.

They can see what happens when somebody clicks on a keyword and comes to their site, including where go, how much time do they spend on each page and what are they engaging with on the site. And they can do the same thing with email as well. Once someone opens the email and clicks through to their site, what they do and what is most important to them can be determined.

By using all those products in one place, and using SiteCatalyst underneath it all, Scotts was able to gain that level of insight. These are relationships that we are pulling together these days, because people want to start to look at online marketing more holistically.

Eric Enge: I believe ExactTarget is the email platform that Scotts is using, and there is an integration of data between email and the search campaign. What are some of the other data sources that can be pulled in and integrated in a fashion like this?

Bill Mungovan: Omniture Genesis is the name of the product that is designed to pull in data from third-party sources. So, ExactTarget is one of many, many email providers we know of.. There is also ad-serving data, which allows display data to also be pulled in.

Another very big category for us is CRM data. By tying actual backend CRM data to upfront advertising, or search engine marketing in particular, you can start to learn a whole lot more about what people do after the lead has been generated. You can also include call center data.

That can be anything that people do with an SAP or an Oracle database, any of those enterprise-level systems which may be point-of-sale data, such as data from a system of kiosks. There are really two types of data: marketing data including online data, such as email and display advertising, and offline marketing data. So, data from the television marketing or any kind of offline media can also be pulled in depending on how it’s structured in whatever system it’s currently in. That’s the one side of the advertising data. The other side would be backend sales data, which is the CRM, Kiosk call center data and the other enterprise systems that may live in an SAP or an Oracle database.

Eric Enge: Right. And there has to be some pretty interesting things going on there to pull in CRM or call center data, which clearly can be massive in size.

Bill Mungovan: Yes, and we have a product called Discover OnPremise for when it does get too big. It’s something we got from Omniture’s acquisition of Visual Sciences. For example, we have a rental car customer who is trying to figure out exactly how many people book online. Then they’ll go to each individual location around the country and observe how many people actually show up to pick up the car they reserved online versus people who don’t. Then they see what people actually buy, how far they drive and all other sorts of data like that. As you can imagine, it just gets absolutely massive at that point.

So Discover OnPremise is a much more powerful and robust tool for when integrations get well beyond the needs of a standard advertiser. But, we do have advertisers who have millions of keywords in SearchCenter and are tying some of those actions back to the systems that don’t have anything to do with what happens on their actual site. So, it starts to get pretty interesting at that point.

Eric Enge: Are there ways to create ties into TV advertising, print advertising and radio advertising?

Bill Mungovan: Well that’s really the million dollar question that every advertising agency in the world is trying to figure out; exactly how does offline data impact online behavior and visa versa? And what we propose to people is to pull that data into SiteCatalyst, start to figure out your own correlations and, if possible, figure out the causality between different marketing programs.

For us, we just provide the repository for the data, and then we allow agencies and advertisers to actually start to figure out what is occurring on a campaign-by-campaign basis. But yes, you can pull that data into SiteCatalyst.

Eric Enge: What are some of the strategies for how you provide the data to SiteCatalyst? And what kind of data is it that you are providing in some of those more difficult scenarios?

Bill Mungovan: I believe CRM data is the right place to focus the discussion because it’s just a little bit more tangible and measurable. For example, Omniture uses SearchCenter for our own marketing efforts in order to get more Omniture customers. A really common scenario for us would be to run an online advertising program and then generate a lead on a web site. But what actually happens to that lead, at least in our case, is that it then goes to a sales force.

The sales force follows it up, and some percentage of those leads actually turn into customers. We track it all the way down to how much we spent to acquire that customer, both online and through our sales team, and then we figure out what we’ve got in return for that. In our case the CRM system we use is Salesforce.com. But there are any number of CRM systems from which we can pull the data.

For us, Cost-Per-Lead is a pathway to one very small piece of the full picture, which will actually help us figure out Profit-Per-Click. So, if you are able to figure out how much you spent on all operating costs, you can pull that data in through CRM integration, and then actually bid on the keywords that lead to the highest profitability for your business.

Those are some of trickiest, but most interesting and most progressive features.

Eric Enge: Let’s talk a little bit about some specific tactics. For example, you know your paid search campaign results in phone call orders. And one tactic you can implement to make tracking much more effective is to give everyone that comes to your search from a paid search ad a custom 800 number. This way you can know the results of your paid search campaign just based on what number they call into. That’s a tactic that is designed to give you much more accurate data.

Bill Mungovan: Yes that makes sense. And another tactic that one of our clients is doing is automatically generating codes on the site itself. This way the customer can actually see that code, so each customer who visits the site from a given campaign will be identified. And we can actually get it all the way down to the keyword level. We know what keywords they came from that led to a call.

Customers see a certain code on the site and then make a phone call and either make purchase or not. Then we have the call center actually take that code in from the customer, so we can record where that customer came from and what they did on the site. Then we can pull that data back into SiteCatalyst and make bidding decisions based on what happened.

Eric Enge: You can also give customers that walk into a physical store a rebate as a part of some promotion that the store is holding. Then they collect the rebate by going online, filling out a form and plugging in the rebate number. Then the web site can check cookies to see if the person came in from a search campaign of some sort.

Bill Mungovan: Yes. But we wouldn’t necessarily be able to tell what specific keyword they came from. It is still a good example, but we’ve actually seen the opposite happen as well. When people come online from a specific keyword they come through to a page and have to print out a coupon that contains a bar code with information in it such as the keyword they searched on, bring it into the store and then redeem it in the store.

What we can see there are two things; how many people print out the coupon and do not go to the store, and how many people print it out and actually redeem it. So that’s just another way of understanding what’s driving people to make offline transactions.

Eric Enge: Exactly. Try to discover every aspect of the interaction that you can.

Bill Mungovan: That’s something really cool that we’ve seen with a retail client. It’s pretty complicated, but it’s very interesting at the same time. The client is able to look through SiteCatalyst as they are running geo-targeted campaigns. Again, these are big box retailers with stores in many different locations, and they are running different ad campaigns for the different geo-locations. And they can see what people are purchasing online and, more importantly, what products people are bundling together in a given geo.

So, there might be a video game and CD on sale in the upper Midwest, and that particular bundling may be very, very different from what people are bundling in Los Angeles. So what they’ve done is taken all the online data and figured out what products people are bundling online. Then they rearrange the actual placements in the store based on what’s happening in that geo. So, when you walk into a store, you would see two products next to each other on the shelf based on what people are doing online in that geography.

Eric Enge: So you basically isolate the best way to put together bundling based on how people are behaving in different areas?

Bill Mungovan: Yes. We figure out what they are buying based on the digital shelf and apply that knowledge to the actual store and rearrange products accordingly.

Eric Enge: What would you recommend to someone running a TV campaign?

Bill Mungovan: It is absolutely critical to pull your TV data into the same system where your online advertising data is running. You should at the very least make sure that you are actually measuring apples-to-apples in one place. So, it’s not an easy question to answer in terms of what TV campaign yields the highest possible return online. That’s a very complicated thing, and it will be different for every customer.

But our advice to the market on that is to pull the data into the same place and then start to run reports on correlations between media in a given geo and what’s happening online.

Eric Enge: You can also try things like Vanity URLs, but things like that are very uncertain.

Bill Mungovan: We have seen a lot of studies that tell us that very few people actually remember your URL address from the end of your television or radio ad, and even fewer go on their computers and actually type it in. For us it’s more interesting to just let the campaigns run separately. So, you have your search campaign online, a display campaign, and then a TV campaign. Then pull the data into one place and use analytics to figure out the correlations.

Say you saw a bump on the 21st of January, you can find out exactly what media was running in which geo, and then you can start to make correlations between the two. So, I think that those tricks of Vanity URLs and things like that don’t work in every case.

Eric Enge: Right. Well, I would think that there is a risk of actually lowering the actual return in return for trying to figure out how to measure it.

Bill Mungovan: That’s right.

Eric Enge: Can you outline how the pricing model works for SearchCenter?

Bill Mungovan: We typically charge as a percent of ad spent so the more you spend, the lower the percentage. We have customers in all shapes and sizes. We’ve had clients take it in-house and then they just felt like they really couldn’t handle it for a while, and they then requested the additional help of our services group.

So we manage it for them for a while, or on an ongoing basis, for an additional percent of ad spend fee. Then after a while we can give it back again when they are ready. We have some flexibility as part of that offering.

Eric Enge: Can you say anything about some other well-known customers that you have using the service, and the total spend you have under management?